The paper deals with speaker verification as a target-nontarget trial task

Abstract

This paper deals with a speaker detection task. In this work, a different interpretation of the
problem is introduced than the one used so far. In the standard approach, each speaker is
modeled by their own model and the task is to decide whether the test speech segment was
generated by the given model or not. In this work, only two models are used: one represents
the target trials and the other represents nontarget trials, where the trial is represented by two
speech segments, both from the same speaker, and two from different speakers, respectively. As
the input features, fixed-length low-dimensional vectors derived from speaker factors generated
by Joint Factor Analysis are used. Gaussian Mixture Models framework is used to model the
feature distribution. The achieved results are compared to the state of the art systems.